Brain activity analyzing apparatus, brain activity analyzing method and biomarker apparatus

ABSTRACT

Provided is a method of analyzing brain activities for realizing a biomarker for neurological/mental disorder, based on brain function imaging. From measured data of resting-state functional connectivity MRI of a healthy group and a patient group, correlation matrix ( 80 ) of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis ( 82 ) on the correlation matrix ( 80 ) and attributes of the subject including a disease/healthy label of the subject. Based on the result of regularized canonical correlation analysis, by discriminant analysis ( 86 ) through sparse logistic regression, a discriminator ( 88 ) is generated.

TECHNICAL FIELD

The present invention relates to a brain activity analyzing apparatus, abrain activity analyzing method and a biomarker apparatus, utilizingfunctional brain imaging.

BACKGROUND ART

(Biomarker)

When biological information is converted into a numerical value andquantified as an index for quantitatively comprehending biologicalchanges in a living body, it is called a “biomarker.”

According to FDA (United States Food and Drug Administration), abiomarker is regarded as “a characteristic that is objectively measuredand evaluated as an indicator of normal biological processes, pathogenicprocesses or pharmacological responses to a therapeutic intervention.”Biomarkers representative of state of disease, changes or degree ofhealing are used as surrogate markers (substitute markers) to monitorefficacy in clinical tests of new drugs. Blood sugar level andcholesterol level are representative biomarkers used as indexes oflifestyle diseases. Biomarkers include not only substances of biologicalorigin contained in urine or blood but also electrocardiogram, bloodpressure, PET images, bone density, lung function and the like.Developments in genomic analysis and proteome analysis have lead todiscovery of various biomarkers related to DNA, RNA or biologicalprotein.

Biomarkers are promising for measuring therapeutic efficacy after theonset of a disease and, in addition, as routine preventive indexes,promising for disease prevention. Further, application of biomarkers toindividualized medicine for selecting effective treatment avoiding sideeffects is expected.

In the field of neurological/mental disorder, however, though studiesdirected to molecular markers and the like usable as objective indexesfrom a biochemical or molecular genetics viewpoint have been made, itwill be justified to say that they are still under consideration.

Meanwhile, a disease determination system using NIRS (Near-InfraRedSpectroscopy), classifying mental disorders such as schizophrenia anddepression based on features of hemoglobin signals measured bybiological optical measurement, is reported (Non-Patent Literature 1).

(Real Time Neurofeedback)

Conventionally, as therapies for Obsessive-Compulsive Disorder (OCD) asone type of neurotic disease, for example, pharmacological andbehavioral treatments have been known. The pharmacological treatmentuses, for example, serotonin-selective reuptake inhibitor. As thebehavioral treatment, exposure response prevention therapy, combiningexposure therapy and response prevention has been known.

Meanwhile, real time neurofeedback is studied as a possible therapy forneurological/mental disorder.

Functional brain imaging, including functional Magnetic ResonanceImaging (fMRI), which visualizes hemodynamic reaction related to humanbrain activities using Magnetic Resonance Imaging (MRI), has been usedto specify an active region of a brain corresponding to a component ofbrain function of interest, that is, to clarify functional localizationof brain, by detecting difference between those in brain activitieswhile responding to a sensory stimulus or performing a cognitive task,and those brain activities in a resting state or while performing acontrol task.

Recently, real time neurofeedback technique using functional brainimaging such as functional magnetic response imaging (fMRI) is reported(Non-Patent Literature 1). Real time neurofeedback technique has come toattract attention as a possible therapy of neurological disorder andmental disorder.

Neurofeedback is one type of bio-feedback, in which a subject receivesfeedback about his/her brain activities and thereby learns a method ofmanaging brain activities.

By way of example, according to a report, activities of anteriorcingulate cortex are measured by fMRI, the measurements are fed back topatients on real time basis as larger or smaller fire image, and thepatients are instructed to make efforts to decrease the size of thefire, then improvement was attained both in real-time and long-termchronic pain of central origin (see Non-Patent Literature 2).

(Resting State fMRI)

Further, recent studies show that even when a subject is in the restingstate, his/her brain works actively. Specifically, in the brain, thereis a group of nerve cells that subside when the brain works actively andare excited vigorously in the resting state. Anatomically, these cellsmainly exist on the medial surface where left and right cerebralhemispheres are connected such as medial aspect of the frontal lobe,posterior cingulate cortex, precuneus, posterior portion of parietalassociation area and middle temporal gyrus. The regions representingbaseline brain activity in the resting state are named Default ModeNetwork (DMN) and these regions work in synchronization as one network(see Non-Patent Literature 3).

An example of difference between brain activities of a healthyindividual and those of a patient of mental disease is observed in brainactivities in the default mode network. The default mode network refersto portions of one's brain that exhibit more positive brain activitieswhen a subject is in the resting state than when the subject isperforming a goal-directed task. It has been reported that abnormalityis observed in the default mode network of patients of mental disordersuch as schizophrenia or Alzheimer's disease as compared with healthyindividuals. By way of example, it is reported that in the brain of aschizophrenia patient, correlation of activities among posteriorcingulate cortex, which belongs to the default mode network, andparietal lateral cortex, medial prefrontal cortex or cerebellar cortex,is decreased in the resting state.

At present, however, it is not necessarily clear how the default modenetwork as such relates to the cognitive function and how thecorrelations of functional connectivity among brain regions relates tothe above-described neurofeedback.

On the other hand, changes in correlations between activities among aplurality of brain regions caused, for example, by difference in tasksare observed, so as to evaluate functional connectivity between thesebrain regions. Specifically, evaluation of functional connectivity inthe resting state obtained by fMRI is referred to as resting-statefunctional connectivity MRI (rs-fcMRI), which is utilized for clinicalstudies directed to various neurological/mental disorders. Theconventional rs-fcMRI, however, is for observing activities of globalneural network such as the default mode network described above, andmore detailed functional connectivity is not yet sufficientlyconsidered.

(DecNef Method: Decoded NeuroFeedback)

On the other hand, a new type neural feedback method referred to asdecoded neurofeedback (DecNef) is reported recently (see Non-PatentLiterature 4).

Human sensory and esthesic systems are ever-changing in accordance withthe surrounding environment. Most of the changes occur in a certainearly period of human developmental stage, or the period referred to asa “critical period.” Adults, however, still keep sufficient degree ofplasticity of sensory and esthesic systems to adapt to significantchanges in surrounding environment. By way of example, it is reportedthat adults subjected to a training using specific esthesic stimulus orexposed to specific esthesic stimulus have improved performance for thetraining task or improved sensitivity to the esthesic stimulus, and thatsuch results of training were maintained for a few months to a few years(see Non-Patent Literature 5). Such a change is referred to as sensorylearning, and it has been confirmed that such a change occurs in everysensory organ, that is, vision, audition, olfaction, gustation, andtaction.

According to DecNef, a stimulus as an object of learning is not directlyapplied to a subject while brain activities are detected and decoded,and only the degree of approximation to a desired brain activity is fedback to the subject to enable “sensory learning.”

(Nuclear Magnetic Resonance Imaging)

Nuclear Magnetic Resonance Imaging will be briefly described in thefollowing.

Conventionally, as a method of imaging cross-sections of the brain orthe whole body of a living body, nuclear magnetic resonance imaging hasbeen used, for example, for human clinical diagnostic imaging, whichmethod utilizes nuclear magnetic resonance with atoms in the livingbody, particularly with atomic nuclei of hydrogen atoms.

As compared with “X-ray CT,” which is a similar method of humantomographic imaging, characteristics of nuclear magnetic resonanceimaging when applied to a human body, for example, are as follows:

(1) An image density distribution reflecting distribution of hydrogenatoms and their signal relaxation time (reflecting strength of atomicbonding) are obtained. Therefore, the shadings present different natureof tissues, making it easier to observe difference in tissues;

(2) The magnetic field is not absorbed by bones. Therefore, a portionsurrounded by a bone or bones (for example, inside one's skull, orspinal cord) can easily be observed; and

(3) Unlike X-ray, it is not harmful to human body and, hence, it has awide range of possible applications.

Nuclear magnetic resonance imaging described above uses magneticproperty of hydrogen atomic nuclei (protons), which are most abundant inhuman cells and have highest magnetism. Motion in a magnetic field ofspin angular momentum associated with the magnetism of hydrogen atomicnucleus is, classically, compared to precession of spin of a spinningtop.

In the following, as a description of background of the presentinvention, the principle of magnetic resonance will be summarized usingthe intuitive classical model.

The direction of spin angular momentum of hydrogen atomic nucleus(direction of axis of rotation of spinning top) is random in anenvironment free of magnetic field. When a static magnetic field isapplied, however, the momentum is aligned with the line of magneticforce.

In this state, when an oscillating magnetic field is superposed and thefrequency of oscillating magnetic field is resonance frequency f0=γB0/2π(γ: substance-specific coefficient) determined by the intensity ofstatic magnetic field, energy moves to the side of atomic nuclei becauseof resonance, and the direction of magnetic vector changes (precessionincreases). When the oscillating magnetic field is turned off in thisstate, the precession gradually returns to the direction in the staticmagnetic field with the tilt angle returning to the previous angle. Byexternally detecting this process by an antenna coil, an NMR signal canbe obtained.

The resonance frequency f0 mentioned above of hydrogen atom is 42.6×B0(MHz) where B0 (T) represents the intensity of the static magneticfield.

Further, in nuclear magnetic resonance imaging, using changes appearingin detected signals in accordance with changes in the blood flow, it ispossible to visualize an active portion of a brain activated in responseto an external stimulus. Such a nuclear magnetic resonance imaging isspecifically referred to as fMRI (functional MRI).

An fMRI uses a common MRI apparatus with additional hardware andsoftware necessary for fMRI measurement.

The change in blood flow causes change in NMR signal intensity, sinceoxygenated hemoglobin has magnetic property different from that ofdeoxygenated hemoglobin. Hemoglobin is diamagnetic when oxygenated, andit does not have any influence on relaxation time of hydrogen atoms inthe surrounding water. In contrast, hemoglobin is paramagnetic whendeoxygenated, and it changes surrounding magnetic field. Therefore, whenthe brain receives any stimulus and local blood flow increases andoxygenated hemoglobin increases, the change can be detected by the MRIsignals. The stimulus to a subject may include visual stimulus, audiostimulus, or performance of a prescribed task (see, for example,Non-Patent Literature 2).

In the studies of brain functions, brain activities are measured bymeasuring increase in nuclear magnetic resonance signal (MRI signal) ofhydrogen atoms corresponding to a phenomenon that density ofdeoxygenated hemoglobin in red blood cells decrease in minute vein orcapillary vessel (BOLD effect).

Particularly, in studies related to human motor function, brainactivities are measured by the MRI apparatus as described above while asubject or subjects are performing some physical activity.

For human subjects, non-invasive measurement of brain functions isessential. In this aspect, decoding technique enabling extraction ofmore detailed information from fMRI data has been developed (see, forexample, Non-Patent Literature 6). Specifically, pixel-by-pixel brainactivity analysis (volumetric pixel: voxel) of brain by the fMRI enablesestimation of stimulus input and state of recognition from spatialpatterns of brain activity. The above-described DecNef is an applicationof such a decoding technique to a task related to sensory learning.

CITATION LIST Patent Literature

-   PTL 1: National Publication No. 2006-132313-   PTL 2: Japanese Patent Laying-Open No. 2011-000184

Non Patent Literature

-   NPL 1: Nikolaus Weiskopf, “Real-time fMRI and its application to    neurofeedback”, NeuroImage 62 (2012) 682-692-   NPL 2: deCharms R C, Maeda F, Glover G H et al, “Control over brain    activation and pain learned by using real-time functional MRI”, Proc    Natl Acad Sci USA 102(51), 18626-18631, 2005-   NPL 3: Raichle M E, Macleod A M, Snyder A Z, et al. “A default mode    of brain function”, Proc Natl Acad Sci USA 98(2), 676-682, 2001-   NPL 4: Kazuhisa Shibata, Takeo Watanabe, Yuka Sasaki, Mitsuo Kawato,    “Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without    Stimulus Presentation”, SCIENCE VOL 334 9 Dec. 2011-   NPL 5: T. Watanabe, J. E. Nanez Sr, S. Koyama, I. Mukai, J.    Liederman and Y. Sasaki: Greater plasticity in lower-level than    higher-level visual motion processing in a passive perceptual    learning task. Nature Neuroscience, 5, 1003-1009, 2002.-   NPL 6: Kamitani Y, Tong F. Decoding the visual and subjective    contents of the human brain. Nat Neurosci. 2005; 8: 679-85.

SUMMARY OF INVENTION Technical Problem

As described above, it is noted that some of the brain activity analysesusing functional brain imaging such as functional magnetic resonanceimaging, and neurofeedback techniques using the same, are applicable totreatment of neurological/mental disorder. These methods and techniques,however, are not yet close to practical use.

When we consider application to treatment of neurological/mentaldisorder, brain activity analysis by functional brain imaging as theabove-described biomarker is promising as non-invasive functionalmarker, and applications to development of diagnostic method and tosearching/identification of target molecule for drug discovery forrealizing basic remedy are also expected.

By way of example, consider mental disorder such as autism. Practicalbiomarker using genes is not yet established and, therefore, developmentof therapeutic agents remains difficult, since it is difficult todetermine effect of medication.

Meanwhile, it has been suggested that diagnostic result of neurologicaldisorder is predicable to some extent based on connections among brainregions derived from fMRI data of the resting state. To verify theprediction performance, however, these studies use only brain functionsmeasured in one facility and, hence, usability as a biomarker has notyet been sufficiently verified.

Therefore, conventionally, it has been unclear how to configure abiomarker utilizing above-described functional brain imaging.

The present invention was made to solve such a problem, and its objectis to provide a brain activity analyzing apparatus and a brain activityanalyzing method that can provide data enabling objective determinationas to whether the state of brain activity is healthy or having adisease.

Another object of the present invention is to provide a brain activityanalyzing apparatus and a brain activity analyzing method for realizinga discrimination process using functional brain imaging to supportdiagnosis of neurological/mental disorder.

A further object of the present invention is to provide a brain activityanalyzing apparatus, a brain activity analyzing method, and a biomarkerapparatus, for realizing a biomarker utilizing functional brain imaging.

Solution to Problem

According to an aspect, the present invention provides a brain activityanalyzing apparatus, including: a discriminator generating means forgenerating a discriminator from signals measured time-sequentially inadvance by a brain activity detecting apparatus detecting signalsindicative of brain activities at a plurality of prescribed regions in abrain of a plurality of subjects, the discriminator generating meansextracting at least a contraction expression common to attributes of theplurality of subjects, from among correlations of brain activities atthe plurality of prescribed regions, and generating a discriminatorrelated to a specific attribute of the attributes of subjects withrespect to the extracted contraction expression; the brain activityanalyzing apparatus further including: a storage device storinginformation for specifying the discriminator; and discriminating meansfor performing a discriminant process on input data based on thediscriminator specified by the information stored in the storage device.

Preferably, the specific attribute is a disease discriminant label.

Preferably, the attributes of subjects include a label of a medicineadministered to the subjects.

Preferably, the brain activity detecting apparatus includes a pluralityof brain activity measuring devices; and the discriminator generatingmeans includes extracting means for extracting the contractionexpression common in attributes of the plurality of subjects andconditions for measurement of the plurality of brain activity measuringdevices, by variable selection from correlations of brain activities atthe plurality of prescribed regions.

Preferably, the discriminator generating means includes regression meansfor generating the discriminator by regression of performing furthervariable selection on the extracted contraction expression.

Preferably, the extracting means includes correlation analyzing meansfor calculating a correlation matrix of activities at the plurality ofprescribed regions from the signals detected by the brain activitydetecting device, and for extracting the contracted expression byexecuting regularized canonical correlation analysis between attributesof the subjects and non-diagonal elements of the correlation matrix.

Preferably, the regression means includes regression analysis means forgenerating a discriminator by sparse logistic regression on a result ofthe regularized canonical correlation analysis and the attributes of thesubjects.

Preferably, the plurality of brain activity measuring devices aredevices for measuring brain activities in a time-series by functionalbrain imaging installed at a plurality of different locations,respectively.

Preferably, the discriminant process is discrimination of a diseaselabel indicating whether the subject is healthy or a patient of aneurological/mental disorder.

Preferably, the attributes of the subjects include a disease labelindicating whether the subject is healthy or a patient of aneurological/mental disorder, a label indicating individual nature ofthe subject, and information characterizing measurement by the brainactivity detecting device; and the discriminant process isdiscrimination of a disease label indicating whether the subject ishealthy or a patient of a neurological/mental disorder.

Preferably, the regularized canonical correlation analysis is acanonical correlation analysis with L1 regularization.

Preferably, the brain activity measuring device picks up a resting-statefunctional connectivity magnetic resonance image.

According to another aspect, the present invention provides a brainactivity analyzing method for a computer including a processing deviceand a storage device to analyze brain activities, including the step of:generating a discriminator from signals measured in a time-series inadvance by a brain activity detecting apparatus detecting signalsindicative of brain activities at a plurality of prescribed regions in abrain of a plurality of subjects; wherein the step of generating thediscriminator includes the step of extracting at least a contractionexpression common to attributes of the plurality of subjects, from amongcorrelations of brain activities at the plurality of prescribed regions,and generating a discriminator related to a specific attribute of theattributes of subjects with respect to the extracted contractionexpression; the method further including the steps of: storinginformation for specifying the discriminator in the storage device; andperforming a discriminant process on input data based on thediscriminator specified by the information stored in the storage device.

Preferably, the specific attribute is a disease discriminant label.

Preferably, the attributes of subjects include a label of a medicineadministered to the subjects.

Preferably, the brain activity detecting apparatus includes a pluralityof brain activity measuring devices; and the discriminator generatingstep includes the step of extracting the contraction expression commonin attributes of the plurality of subjects and conditions formeasurement of the plurality of brain activity measuring devices, byvariable selection from correlations of brain activities at theplurality of prescribed regions.

Preferably, the discriminator generating step includes the step ofgenerating the discriminator by regression of performing furthervariable selection on the extracted contraction expression.

According to a still further aspect, the present invention provides abiomarker apparatus for generating an output as a biomarker by computeranalysis of brain activities, including a storage device for storinginformation for specifying a discriminator; wherein the discriminator iscalculated from signals measured in a time-series in advance by a brainactivity detecting apparatus detecting signals indicative of brainactivities at a plurality of prescribed regions in a brain of aplurality of subjects, by extracting at least a contraction expressioncommon to attributes of the plurality of subjects, from amongcorrelations of brain activities at the plurality of prescribed regions,and generated for a disease discrimination label of the attributes ofsubjects with respect to the extracted contraction expression; theapparatus further including a processing device configured to perform adiscriminant process on input data based on the discriminator specifiedby the information stored in the storage device.

Preferably, the attributes of subjects include a label of a medicineadministered to the subjects.

Advantageous Effects of Invention

By the present invention, it becomes possible to realize a brainactivity analyzing apparatus and a brain activity analyzing method thatcan provide data enabling objective determination as to whether thestate of brain activity is healthy or having a disease.

Further, by the present invention, it becomes possible to realize abiomarker utilizing functional brain imaging, for neurological/mentaldisorder.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram showing an overall configuration of an MRIapparatus 10.

FIG. 2 is a hardware block diagram of data processing unit 32.

FIG. 3 shows regions of interest (ROI) of a brain imaged by rs-fcMRI inaccordance with an embodiment.

FIG. 4 shows a concept of a procedure for extracting correlation matrixrepresenting correlations of functional connectivity in the restingstate.

FIG. 5 shows a concept of a process for generating a discriminatorserving as a biomarker, from the correlation matrix.

FIG. 6 is a flowchart representing a process executed by data processingunit 32 for generating the discriminator serving as the biomarker.

FIG. 7 shows features obtained by Sparse Canonical Correlation Analysis(SCCA) (SCCA feature space) and a concept of a procedure for generatingthe discriminator by and Sparce Logistic Regression (SLR) using thefeatures as an input.

FIG. 8 shows features (SCCA feature space) obtained by SCCA and aconcept of a procedure for generating the discriminator by SLR using thefeatures as an input.

FIG. 9 shows features (SCCA feature space) obtained by SCCA and aconcept of a procedure for generating the discriminator using by SLRusing the features as an input.

FIG. 10 shows a concept of generating a biomarker.

FIG. 11 shows a concept of verifying the generated biomarker.

FIG. 12 shows properties of the biomarker.

FIG. 13 shows a concept of a multi-disease biomarker.

DESCRIPTION OF EMBODIMENTS

In the following, a configuration of an MRI system in accordance withembodiments of the present invention will be described with reference tothe drawings. In the embodiments below, components or process stepsdenoted by the same reference characters are the same or correspondingcomponents or steps and, therefore, description thereof will not berepeated unless necessary.

[First Embodiment]

FIG. 1 is a schematic diagram showing an overall configuration of an MRIapparatus 10.

Referring to FIG. 1, MRI apparatus 10 includes: a magnetic fieldapplying mechanism 11 applying a controlled magnetic field to, andirradiating with RF wave, a region of interest of a subject 2; areceiving coil 20 receiving a response wave (NMR signal) from subject 2and outputting an analog signal; a driving unit 21 controlling themagnetic field applied to subject 2 and controllingtransmission/reception of RF wave; and a data processing unit 32configuring a control sequence of driving unit 21 and processing variousdata signals to generate an image.

Here, a central axis of a cylindrical bore in which subject 2 is placedis regarded as a Z-axis, and a horizontal direction orthogonal to theZ-axis and the vertical direction orthogonal to the Z-axis are definedas X-axis and Y-axis, respectively.

In MRI apparatus 10 having such a configuration, because of the staticmagnetic field applied by magnetic field applying mechanism 11, nuclearspins of atomic nuclei forming subject 2 are oriented in the directionof magnetic field (Z-axis) and perform precession with the direction ofmagnetic field being an axis, with Larmor frequency unique to the atomicnuclei.

When irradiated with an RF pulse of the same Larmor frequency, the atomsresonate, absorb energy and are excited, resulting in nuclear magneticresonance (NMR). When the irradiation with RF pulse is stopped after theresonance, the atoms discharge energy and return to the original, steadystate. This process is referred to as a relaxation process. In therelaxation process, the atoms output electromagnetic wave (NMR signal)having the same frequency as the Larmor frequency.

The output NMR signal is received by receiving coil 20 as a responsewave from subject 2, and the region of interest of subject 2 is imagedby data processing unit 32.

Magnetic field applying mechanism 11 includes a static magnetic fieldgenerating coil 12, a magnetic field gradient generating coil 14, an RFirradiating unit 16, and a bed 18 for placing subject 2 in the bore.

By way of example, subject 2 lies on his/her back on bed 18. Though notlimited, subject 2 may view an image displayed on a display 6 mountedvertical to the Z-axis, using prism glasses 4. Visual stimulus isapplied to subject 2 by an image on display 6. In another embodiment,visual stimulus to subject 2 may be applied by projecting an image infront of subject 2 using a projector.

Such a visual stimulus corresponds to presentation of feedbackinformation in the above-described neurofeedback.

Driving unit 21 includes a static magnetic field power source 22, amagnetic field gradient power source 24, a signal transmitting unit 26,a signal receiving unit 28, and a bed driving unit 30 for moving bed 18to any position along the Z-axis.

Data processing unit 32 includes: an input unit 40 for receiving variousoperations and information input from an operator (not shown); a displayunit 38 for displaying various images and various pieces of informationrelated to the region of interest of subject 2, on a screen; a displaycontrol unit 34 for controlling display of display unit 38; a storageunit 36 for storing programs to cause execution of various processes,control parameters, image data (structural images and the like) andother electronic data; a control unit 42 controlling operations ofvarious functional units, including generating a control sequence fordriving the driving unit 21; an interface unit 44 for executingtransmission/reception of various signals to/from driving unit 21; adata collecting unit 46 for collecting data consisting of a group of NMRsignals derived from the regions of interest; an image processing unit48 for forming an image based on the data of NMR signals; and a networkinterface 50 for executing communication with a network.

Data processing unit 32 may be a dedicated computer, or it may be ageneral purpose computer executing functions of causing operations ofvarious functional units, in which designated operations, dataprocessing and generation of control sequence are realized by a programor programs stored in storage unit 36. In the following, descriptionwill be given assuming that data processing unit 32 is implemented by ageneral purpose computer.

Static magnetic field generating coil 12 causes a current supplied froma static magnetic field power source 22 to flow through a helical coilwound around the Z-axis to generate an induction magnetic field, andthereby generates a static magnetic field in the Z-direction in thebore. The region of interest of subject 2 is placed in the region ofhighly uniform static magnetic field formed in the bore. Morespecifically, here, static magnetic field generating coil 12 iscomprised of four air core coils, forms a uniform magnetic field insideby the combination of the coils, and attains orientation of the spins ofprescribed atomic nuclei in the body of subject 2, or more specifically,the spins of hydrogen atomic nuclei.

Magnetic field gradient generating coil 14 is formed of X-, Y- andZ-coils (not shown), and provided on an inner peripheral surface ofcylindrical static magnetic field generating coil 12.

These X-, Y- and Z-coils superpose magnetic field gradients on theuniform magnetic field in the bore with the X-axis, Y-axis and Z-axisdirections switched in turn, whereby creating intensity gradient in thestatic magnetic field. When excited, the Z-coil tilts the magnetic fieldintensity to the Z-direction and thereby defines a resonance surface;the Y-coil applies a tilt for a short period of time immediately afterapplication of the magnetic field in the Z-direction, and thereby addsphase modulation in proportion to the Y-coordinate, to the detectedsignal (phase encoding); and thereafter the X-coil applies a tilt whendata is collected, and thereby adds frequency modulation in proportionto the X-coordinate, to the detected signal (frequency encoding).

The switching of superposed magnetic field gradients is realized asdifferent pulse signals are output to the X-, Y- and Z-coils from themagnetic field gradient power source 24 in accordance with a controlsequence. Thus, the position of subject 2 expressed by the NMR can bespecified, and positional information in three-dimensional coordinatesnecessary for forming an image of subject 2 are provided.

Here, using the orthogonally crossing three sets of magnetic fieldgradients, allocating slice direction, phase encoding direction andfrequency encoding direction to the magnetic fields respectively and bycombining these, images can be taken from various angles. By way ofexample, in addition to transverse slice in the same direction as takenby an X-ray CT apparatus, saggital and coronal slices orthogonalthereto, as well as an oblique slice, of which direction vertical to itsplane is not parallel to any of the axes of three orthogonally crossingmagnetic field gradients, can be imaged.

RF irradiating unit 16 irradiates a region of interest of subject 2 withRF (Radio Frequency) pulses based on a high-frequency signal transmittedfrom a signal transmitting unit 26 in accordance with a controlsequence.

Though RF irradiating unit 16 is built in magnetic field applyingmechanism 11 in FIG. 1, it may be mounted on bed 18 or integrated withreceiving coil 20.

Receiving coil 20 detects a response wave (NMR signal) from subject 2,and in order to detect the NMR signal with high sensitivity, it isarranged close to subject 2.

Here, when an electromagnetic wave of NMR signal crosses a coil strandof receiving coil 20, a weak current is generated by electromagneticinduction. The weak current is amplified by signal receiving unit 28 andconverted from an analog signal to a digital signal, and thentransmitted to data processing unit 32.

The mechanism here is as follows. To a subject 2 in a state of staticmagnetic field with Z-axis magnetic field gradient added, ahigh-frequency electromagnetic field of resonance frequency is appliedthrough RF irradiating unit 16. Prescribed atomic nuclei at a portionwhere magnetic field intensity satisfies the condition of resonance, forexample, hydrogen atomic nuclei, are selectively excited and startresonating.

Prescribed atomic nuclei at a portion satisfying the condition ofresonance (for example, a slice of prescribed thickness of subject 2)are excited, and spin axes of atomic nuclei concurrently startprecession. When the excitation pulse is stopped, electromagnetic wavesirradiated by the atomic nuclei in precession induce a signal inreceiving coil 20 and, for some time, this signal is continuouslydetected. By this signal, a tissue containing the prescribed atoms inthe body of subject 2 is monitored. In order to know the position wherethe signal comes from, X- and Y-magnetic field gradients are added andthe signal is detected.

Based on the data built in storage unit 36, image processing unit 48measures detected signals while repeatedly applying excitation signals,reduces resonance frequency to X-coordinate by a first Fouriertransform, restores Y-coordinate by a second Fourier transform, andthus, displays a corresponding image on display unit 38.

For example, by picking-up the above-described BOLD signal on real-timebasis using the MRI system as described above and performing ananalysis, which will be described later, on the time-sequentiallypicked-up images by control unit 42, it is possible to takeresting-state functional connectivity MRI (rs-fcMRI).

FIG. 2 is a hardware block diagram of data processing unit 32.

Though the hardware of data processing unit 32 is not specificallylimited as described above, a general-purpose computer may be used.

Referring to FIG. 2, a computer main body 2010 of data processing unit32 includes, in addition to a memory drive 2020 and a disk drive 2030, aCPU 2040, a bus 2050 connected to disk drive 2030 and memory drive 2020,an ROM 2060 for storing programs such as a boot-up program, an RAM 2070for temporarily storing instructions of an application program andproviding a temporary memory space, a non-volatile storage device 2080for storing an application program, a system program and data, and acommunication interface 2090. Communication interface 2090 correspondsto an interface unit 44 for transmitting/receiving signals to/fromdriving unit 21 and the like and a network interface 50 forcommunicating with another computer through a network, not shown. Asnon-volatile storage device 2080, a hard disk (HDD), a solid state drive(SSD) or the like may be used.

By operation processes executed by CPU 2040 in accordance with aprogram, various functions of data processing unit 32 includingfunctions of control unit 42, data collecting unit 46 and imageprocessing unit 48 are realized.

A program or programs causing data processing unit 32 to execute thefunction of the present embodiment as described above may be stored in aCD-ROM 2200 or a memory medium 2210 and inserted to disk drive 2030 ormemory drive 2020 and may further by transferred to non-volatile storagedevice 2080. The program is loaded to RAM 2070 before execution.

Data processing unit 32 further includes a keyboard 2100 and a mouse2110 as input devices, and a display 2120 as an output device. Keyboard2100 and mouse 2110 correspond to input unit 40 and display 2120corresponds to display unit 38.

The program realizing the function of data processing unit 32 asdescribed above may not necessarily include an operating system (OS) forexecuting the function of information processing apparatus such ascomputer main body 2010. The program may only include those portions ofinstructions which can call appropriate functions (modules) in acontrolled manner to attain a desired result. The manner how dataprocessing unit 32 operates is well known and, therefore, detaileddescription will not be given here.

It is noted that one or a plurality of computers may be used to executethe program described above. In other words, either centralized ordistributed processing may be possible.

FIG. 3 shows regions of interest (ROI) of a brain imaged by rs-fcMRI inaccordance with an embodiment.

Here, a biomarker related to Autistic Spectrum Disorder (ASD) will bedescribed as an example, and ninety-three regions are used as regions ofinterest.

Such regions of interest include, by way of example, the following:

Dorsomedial Prefrontal Cortex (DMPFC);

Ventromedial Prefrontal Cortex (VMPFC);

Anterior Cingulate Cortex (ACC);

Cerebellar Vermis;

Left Thalamus;

Right Inferior Parietal Lobe;

Right Caudate Nucleus;

Right Middle Occipital Lobe; and

Right Middle Cingulate Cortex.

It is noted, however, that the brain regions used may not be limited tothose above.

For instance, the regions to be selected may be changed in accordancewith the neurological/mental disorder to be studied.

FIG. 4 shows a concept of a procedure for extracting correlation matrixrepresenting correlations of functional connectivity in the restingstate, from the regions of interest such as shown in FIG. 3.

Referring to FIG. 4, from fMRI data of n (n: natural number) time pointsin the resting state measured on real-time basis, average “degree ofactivity” of each region of interest is calculated, and correlationsamong the brain regions (among the regions of interest) are calculated.

Here, ninety-three regions are picked up as regions of interest and,therefore, the number of independent non-diagonal elements in thecorrelation matrix will be, considering the symmetry,(93×93−93)/2=4278.In FIG. 4, only the correlations of 34×34 are shown.

FIG. 5 shows a concept of a process for generating a discriminatorserving as a biomarker, from the correlation matrix described withreference to FIG. 4.

Referring to FIG. 5, from data of resting-state functional connectivityMRI obtained by measuring a group of healthy subjects (in this example,114 individuals) and a group of patients (in this example, seventy-fourindividuals), data processing unit 32 derives correlation matrix 80 ofdegree of activity among brain regions (regions of interest) inaccordance with a procedure that will be described later.

Thereafter, by data processing unit 32, feature extraction 84 isperformed by regularized canonical correlation analysis 82 on thecorrelation matrix and on the attributes of subjects includingdisease/healthy labels of the subjects. Here, “regularization” generallyrefers to a method of preventing over-learning by adding aregularization term to an error function in machine learning andstatistics and thereby restricting complexity/degree of freedom of amodel. If the result of regularized canonical correlation analysisresults in sparse explanatory variables this process will bespecifically referred to as sparse canonical correlation analysis(SCCA). In the following, an example employing SCCA will be described.

Further, by data processing unit 32, based on the result of regularizedcanonical correlation analysis, a discriminator 88 is generated fromdiscriminant analysis 86 by sparse logistic regression.

As will be described later, the data of healthy group and the patientgroup are not limited to those measured by the MRI itself. Data measuredby a different MRI apparatus may also be integrated, to generate thediscriminator. Generally speaking, data processing unit 32 may notnecessarily be a computer for executing control of the MRI apparatus,and it may be a computer specialized in generating the discriminator byreceiving measurements data from a plurality of MRI apparatuses andperforming the discriminant process by using the generateddiscriminator.

FIG. 6 is a flowchart representing a process executed by data processingunit 32 for generating the discriminator serving as the biomarker.

In the following, the process described with reference to FIG. 5 will bediscussed in greater detail with reference to FIG. 6.

The biggest problem posed when a biomarker is to be generated based onthe discriminant label of a disease of a subject and connections ofbrain regions derived from the fMRI data in the resting state is thatthe number of data dimensions is overwhelmingly larger than the numberof data. Therefore, if training of a discriminator for predicting thedisease discriminant label (here, the label indicating whether thesubject has the disease or healthy will be referred to as the “diseasediscriminant label”) is done using a data set without regularization,the discriminator will be over-fitted, and the prediction performancefor unknown data significantly decreases.

Generally, in machine learning, a process to enable explanation ofobserved data with a smaller number of explanatory variables is referredto as “variable selection (or feature extraction).” In the presentembodiment, “extraction of contraction expression” refers to variableselection (feature extraction) to enable formation of the discriminatorwith smaller number of correlation values in the machine learning of thediscriminator for predicting the discriminant label of a disease to bestudied from among “a plurality of correlation values (a plurality ofconnections) of the degree of activity among brain regions (regions ofinterest),” that is, to select correlation values of higher importanceas the explanatory variables.

In the present embodiment, as the method of feature extraction,regularization is adopted. In this manner, canonical correlationanalysis is performed with regularization and obtaining sparsevariables, so as to leave explanatory variables of higher importance.This process is referred to as sparse canonical correlation analysis.More specifically, as the method of regularization also results insparse variables, we can use a method of imposing a penalty to themagnitude of absolute value of parameters for canonical correlationanalysis referred to as “L1 regularization” as will be described in thefollowing.

Specifically, referring to FIG. 6, when the process for generating thediscriminator starts (S100), data processing unit 32 reads rs-fcMRI dataof each subject from storage unit 36 (S102), and performs featureextraction by SCCA (S104).

In the following, L1 regularization canonical correlation analysis willbe described. As to the L1 regularization canonical correlationanalysis, see, for example, the reference below.

-   Reference: Witten D M, Tibshirani R, and T Hastie. A penalized    matrix decomposition, with applications to sparse principal    components and canonical correlation analysis. Biostatistics, Vol.    10, No. 3, pp. 515-534, 2009.

First, in general Canonical Correlation Analysis (CCA), a data pair x₁and x₂ as given below is considered. It is noted that the variables x₁and x₂ are standardized to have an average zero and standard deviationone. Further, it is assumed that each of the data pair x₁ and x₂ has thedata number of n.x ₁ ∈R ^(n×p) ¹ ,x ₂ ∈R ^(n×p) ²Here, according to CCA, parameters w₁∈R^(p) ¹ , w₂∈R^(p) ² that canmaximize the correlation between z₁=x₁w₁, z₂=x₂w₂ are calculated. Thatis, the following optimization problem is solved.Under the condition of

w₁^(T)x₁^(T)x₁w₁ = w₂^(T)x₂^(T)x₂w₂ = 1$\max\limits_{w_{1},w_{2}}{w_{1}^{T}x_{1}^{T}x_{2}{w_{2}.}}$

In contrast, by introducing L1 regularization, the process will be tosolve the following optimization problem.

Under the condition of

${{w_{1}}^{2} \leq 1},{{w_{2}}^{2} \leq 1},{{w_{1}}_{1} \leq c_{1}},{{w_{2}}_{1} \leq c_{2}},{\max\limits_{w_{1},w_{2}}{w_{1}^{T}x_{1}^{T}x_{2}w_{2}}}$

Here, c₁ and c₂ are parameters representing the strength of L1regularization, and they are set appropriately in accordance with thedata by various known methods. The suffix “1” on the lower right side of∥w₁∥₁ and ∥w₂∥₁ represents that ∥w₁∥ and ∥w₂∥ are L1 norms.

As the constraint condition for L1 regularization is added, the valuesof those elements of lower importance of the elements of parameters w₁and w₂ will become zero and, hence, the features (explanatory variables)become sparse.

Thereafter, data processing unit 32 performs discriminant analysis bySLR based on the result of SCCA (S106).

SLR refers to a method of logistic regression analysis expanded to aframe of Bayes' estimation, in which dimensional compression of afeature vector is performed concurrently with weight estimation fordiscriminant. This is useful when the feature vector of data has a verylarge number of dimensions and includes many unnecessary featureelements. For unnecessary feature elements, weight parameter in lineardiscriminant analysis will be set to zero (that is, variable selectionis done), so that only a limited number of feature elements related tothe discriminant are extracted (sparseness).

In SLR, probability p of obtained feature data belonging to a class iscalculated class by class, and the feature data is classified to theclass corresponding to the highest output value p. The value p is outputby a logistic regression equation. Weight estimation is done by ARD(Automatic Relevance Determination), and feature element lesscontributing to class determination is removed from the calculation asits weight comes closer to zero.

Specifically, using the features extracted by the L1 regularization CCAas described above, the discriminator based on the hierarchical Bayes'estimation as will be described in the following estimates thedisease/healthy label.

Here, assuming that the features z₁=w₁x₁ derived from CCA are input toSLR and S={0, 1} is a label of disease (S=1)/healthy (S=0), then, theprobability of the SLR output being S=1 is defined as follows.

$\begin{matrix}{{p\left( {z_{1},w} \right)} = \frac{1}{1 + e^{{- w^{T}}z_{1}}}} & (1)\end{matrix}$

Here, the distribution of parameter vector w is set to the normaldistribution as given below. In the equation, α represents a hyperparameter vector representing variance of normal distribution of vectorw.p(w|α)=N(w|0,diag(α))

Further, by setting the distribution of hyper parameter vector α asfollows, distribution of each parameter is estimated by hierarchicalBayes' estimation.

${p(\alpha)} = {\underset{j}{\Pi}\;{\Gamma\left( {{\alpha_{i}❘a^{0}},b^{0}} \right)}}$

Here, Γ represents a Γ distribution, and a⁰ and b⁰ are parametersdetermining gamma distribution of hyper parameter vector α. The i-thelement of vector α is represented by α_(i).

As to the SLR, see, for example, the reference below.

-   Reference: Okito Yamashita, Masaaki Sato, Taku Yoshioka, Frank Tong,    and Yukiyasu Kamitani. “Sparse Estimation automatically selects    voxels relevant for the decoding of fMRI activity patterns.”    NeuroImage, Vol. 42, No. 4, pp. 1414-1429, 2008.

Based on the result of discriminant analysis as described above, thediscriminator that discriminates the disease/healthy label (label fordiscriminating the disease) on the input features is generated (S108).The information for specifying the generated discriminator (data relatedto the function form and parameters) is stored in storage unit 36, and,when test data is input later, will be used for the discriminant processwhen the discriminant label of the disease is estimated for the testdata.

Specifically, based on doctors' diagnosis in advance, the subjects aredivided to a group of healthy individuals and a group of patients.Correlations (connectivity) of degree of activities among brain regions(regions of interest) of the subjects will be measured. By machinelearning of measurement results, the discriminator is generated todiscriminate whether test data of a new subject, different from thoseabove, fits to disease or healthy. The discriminator functions as abiomarker of mental disorder. Here, the “disease discriminant label” asthe biomarker output may include a probability that the subject has thedisease (or probability that the subject is healthy), since thediscriminator is generated by logistic regression. By way of example, anindication “Probability of having the disease: oo %” may be output. Sucha probability may be used as a “diagnosis marker.”

As to the attribute to be the output of the discriminator is notnecessarily limited to discrimination of a disease, and it may be anoutput related to a different attribute.

In that case also, a discrete determination result indicating to whichclass it belongs may be output, or a continuous value such as aprobability of the attribute belonging to a certain class may be output.

In summary, in biomarker learning (generation), in order to generate thebiomarker for mental disorder, data of resting state functionalconnectivity MRI (rs-fcMRI) is used as an input, feature extraction isdone by L1 regularization CA as described above, and using the extractedfeatures as the input, disease/healthy discriminant is done by the SLR.

In the foregoing, it is assumed that the features z₁=w₁x₁ derived fromCCA are used as the features to be the input to SLR.

The features to be used as the input to SLR, however, are not limited tothe above.

FIGS. 7 to 9 show a concept of a procedure for generating thediscriminator using features obtained by SCCA (SCCA feature space) andSLR using the features as an input.

First, as shown in FIG. 7, as a first method, using healthy/diseaselabel as an attribute of subjects, SCCA is executed between the labeland the elements of correlation matrix obtained from rs-fcMRI.

The resulting features (intermediate expression) z₁=w₁x₁ (canonicalvariance, two dimensions) are used as an input to SLR, and thediscriminator is generated (hereinafter this procedure will be referredto as “method A”).

Here, “canonical variance, two dimensions” means that two dimensionalvectors of disease (1, 0) and healthy (0, 1) are used.

Here, of 4278 of non-diagonal elements of correlation matrix,approximately 700 elements (connections) are selected by SCCA.

Specifically, using certain data of disease/healthy diagnosis results,parameters of discriminator are determined in advance by machinelearning such as SCCA, SLR or the like. The determined parameters aregeneralized for unknown rs-fcMRI data, and thus, the discriminatorfunctions as a biomarker. For the discriminator, linear sum of selectedconnections of rs-fcMRI is applied as an input, and the disease label(1, 0) and the healthy label (0, 1) correspond to the output.

Referring to FIG. 8, as the second method, the following procedure maybe adopted. Attributes of subjects include the disease/healthy label,age of the subject, sex of the subject, measurement agency (the placewhere the measuring apparatus is placed, referred to as measurementsite; in the example described later, the number of measurement sites isthree), intensity of magnetic field applied by the fMRI apparatus(corresponding to one of the indexes of performance such as theresolution of imaging apparatus), and conditions of experiments such aseyes opened/closed at the time of imaging are used. SCCA is executedbetween these attributes and the elements of correlation matrix obtainedfrom rs-fcMRI. As the index of performance of fMRI imaging apparatus,not only the intensity of applied magnetic field but also other indexesmay be used.

In other words, as the “explanatory variables” of canonical correlationanalysis, independent elements of correlation matrix from rs-fcMRI areadopted, and as the “criterion variables,” the attributes of subjectsuch as described above are adopted.

The resulting features (intermediate expression) z₁=w₁x₁ (canonicalvariance, eleven dimensions) are used as an input to SLR, and thediscriminator is generated (hereinafter this procedure will be referredto as “method B”).

Here, “canonical variance, eleven dimensions” means a sum of elevendimensions including disease/healthy (two dimensions), three sites(three dimensions: (1, 0, 0), (0, 1, 0), (0, 0, 1)), age (onedimension), sex (two dimensions (1, 0), (0, 1)), performance (onedimension), eyes opened/closed (two dimensions: (1, 0), (0, 1)).

FIG. 9 shows a third method. In the third method, as attributes ofsubjects, the disease/healthy label, age of the subject, sex of thesubject, measurement agency (measurement site), performance of fMRIapparatus (example: intensity of magnetic field applied), and conditionsof experiments such as eyes opened/closed at the time of imaging areused. SCCA is executed between these attributes and the elements ofcorrelation matrix obtained from rs-fcMRI. This procedure is the same asmethod B.

Sparsely selected non-diagonal elements of rs-fcMRI as the result ofSCCA are used as the input of SLR, and thus the discriminator isgenerated (in the following, this procedure will be referred to as“method C”).

Here, in SCCA, only the non-diagonal elements of correlation matrix ofrs-fcMRI related to the disease/healthy label are selected. This processfilters out rs-fcMRI data expressing other factors, and thus, abiomarker independent of the agency where the data is obtained can beobtained.

By performing SCCA using influences of different sites, experimentalconditions and the like, it becomes possible to find which connection isrelated to which factor. Therefore, it becomes possible to excludeconnections unrelated to the disease/healthy label, and to derive onlythe connections related to the disease/healthy label. Thisadvantageously leads to “generalization” of the biomarker.

Further, by sparse method of SLR, for example, of approximately 700connections made sparse by SCCA, twenty-one connections are used for thediscriminator by the method C.

In methods B and C described above, attributes to the subject mayinclude a label indicating whether or not a prescribed medicine has beenadministered, or information of dosage or duration of administration ofsuch medicine.

FIG. 10 shows a concept of such a procedure for generating thebiomarker.

At three measurement agencies of Site one to Site three, using MRIapparatuses having different measurement performances, rs-fcMRI data ofthe group of 114 healthy individuals and the group of seventy-fourpatients were obtained, and from the resulting correlation matrix havingmore than 4000 connections, the discriminator is generated by the SCCAand SLR processes.

(Verification of Biomarker)

FIG. 11 shows a concept of the procedure for verifying the biomarkergenerated in the manner as described above.

Using the parameters for feature extraction and discriminant obtained bylearning based on the data collected at Sites one to three in Japan,disease/healthy label is predicted for the data not used for theparameter learning, and consistency with the actual disease discriminantlabel is evaluated.

FIG. 12 shows properties of the biomarker.

As a result of consistency evaluation mentioned above, by way ofexample, when method C described above was used, the discriminantperformance of 79% or higher at the highest was observed for the data ofthree different sites in Japan. When the biomarker using the sameparameter was evaluated with the rs-fcMRI data measured at six sites inthe United States, the discriminant performance was 66% or higher.

In FIG. 12, Site four collectively represents the six sites in theUnited States.

In FIG. 12, “sensitivity” refers to the probability of correctly testingpositive (having the disease) the subjects having the disease, and the“specificity” refers to the probability of correctly testing negative(not having the disease) the healthy subjects. “Positive likelihoodratio” refers to the ratio of true positive to false positive, and“negative likelihood ratio” refers to the ratio of false negative totrue negative. DOR is diagnostic odds ratio, representing the ratio ofsensitivity to specificity.

In FIG. 12, the generalization to the sites in the United States showsthe high performance of the biomarker obtained by learning from limiteddata of subjects, in the sense that it goes beyond the race-by-race orcountry-by-country diagnostic criteria.

(Multi-disease Biomarker)

In the foregoing, among the criterion variables of SCCA, thedisease/healthy level is related to one disease.

However, when labels of a plurality of diseases are used as diseaselabels when rs-fcMRI data is used as the explanatory variable, it ispossible to use the biomarker as multi-disease biomarker.

Specifically, here, the disease labels include labels for discriminatinga disease from healthy state for each of a plurality of diseases.

FIG. 13 shows a concept of such a multi-disease biomarker.

FIG. 13 shows only a hypothetical example. However, if the relationsamong diseases that have been called by different names influences ofmedications and the like administered for various diseases and brainactivities are studied using (SCCA+SLR) method on the rs-fcMIR datadescribed above, it becomes possible to extract correlations betweeneach of these.

By way of example, the disease label mentioned above may include adisease/healthy label of “autism”, a disease/healthy label of“schizophrenia”, a disease/healthy label of “depression” and adisease/healthy label of “obsessive/compulsive disorder.”

The medication labels may include information related to types ofmedication such as “psychotropic drug 1” and “psychotropic drug 2” aswell as dosage and administration period of each drug.

The conditions of imaging includes performance of fMRI imaging apparatusas described above (example: intensity of applied magnetic field) andinformation related to whether measurement was done with eyes of thesubject opened/closed.

Further, individual attributes of a subject include information relatedto age, sex and the like.

In the foregoing description, it is assumed that real-time fMRI is usedas the brain activity detecting apparatus for time-sequentiallymeasuring brain activities by functional brain imaging. It is noted,however, that any of the fMRI described above, a magnetoencephalography,a near-infrared spectroscopy (NIRS), an electroencephalography or acombination of any of these may be used as the brain activity detectingapparatus. Regarding such a combination, it is noted that fMRI and NIRSdetect signals related to change in blood flow in the brain, and havehigh spatial resolution. On the other hand, magnetoencephalography andelectroencephalography are characterized in that they have high temporalresolution, for detecting change in electromagnetic field associatedwith the brain activities. Therefore, if fMRI and themagnetoencephalography are combined, brain activities can be measuredwith both spatially and temporally high resolutions. Alternatively, bycombining NIRS and the electroencephalography, a system for measuringbrain activities with both spatially and temporally high resolutions canalso be implemented in a small, portable size.

By the configuration as described above, it becomes possible to realizea brain function analyzing apparatus and a brain function analyzingmethod functioning as biomarkers using functional brain imaging, inrelation to neurological/mental disorder.

In the foregoing, an example has been described in which a “diseasediscriminant label” is included as an attribute of a subject, and bygenerating a discriminator through machine learning, the discriminatoris caused to function as a biomarker. The present invention, however, isnot necessarily limited to such an example. Provided that a group ofsubjects whose results of measurements are to be obtained as the objectof machine learning is classified into a plurality of classes in advanceby an objective method, the correlation of degree of activity(connectivity) among brain regions (regions of interest) of the subjectsis measured and a discriminator can be generated for classification bymachine learning using the measured results, the present invention maybe used for other discrimination.

Further, as described above, such discrimination may indicatepossibility of belonging to a certain attribute, as a probability.

Therefore, whether a certain “training” or a “behavioral pattern” ishelpful to increase health of a subject or not can objectively beevaluated. Even when a subject does not yet have a disease (in as state“before the onset of a disease”), it is possible to objectively evaluatewhether substance to be ingested such as “food” and “drink” or a certainactivity is effective to attain a healthier state.

If an indication such as “probability of how healthy you are: oo %” isgiven in the state before the onset of a disease mentioned above, it ispossible to indicate the user of his/her health conditions by anobjective numerical value. Here, the output need not be the probability,and “continuous value representing the degree of how healthy you are,for example, probability of being healthy” converted to a score may bedisplayed. By such a display, the embodiment of the present inventioncan be used not only as a support for diagnosis but also as an apparatusfor health management.

The embodiments as have been described here are mere examples and shouldnot be interpreted as restrictive. The scope of the present invention isdetermined by each of the claims with appropriate consideration of thewritten description of the embodiments and embraces modifications withinthe meaning of and equivalent to, the languages in the claims.

REFERENCE SIGNS LIST

2 subject, 6 display, 10 MRI apparatus, 11 magnetic field applyingmechanism, 12 static magnetic field generating coil, 14 magnetic fieldgradient generating coil, 16 RF irradiating unit, 18 bed, 20 receivingcoil, 21 driving unit, 22 static magnetic field power source, 24magnetic field gradient power source, 26 signal transmitting unit, 28signal receiving unit, 30 bed driving unit, 32 data processing unit, 36storage unit, 38 display unit, 40 input unit, 42 control unit, 44interface unit, 46 data collecting unit, 48 image processing unit, 50network interface.

The invention claimed is:
 1. A brain activity analyzing apparatusreceiving brain activity signals indicative of brain activity andmeasured time-sequentially by a brain activity detecting apparatus,comprising: a storage storing information including the brain activitysignals indicative of brain activities in a resting state at a pluralityof prescribed regions in a brain of each of a plurality of firstsubjects; a processor configured to, during a training process; sparselyextract by a regularized canonical correlation analysis algorithm, acontraction expression common to said plurality of first subjects withrespect to a plurality of types of attributes of each of said pluralityof first subjects, said contraction expression expressing a combinationof sparsely extracted correlations between said plurality of prescribedregions in said brain activity signals stored in said storage, andgenerate a discriminator to perform a discriminant process of a firstattribute among said plurality of types of attributes based on saidextracted correlations, and store information for specifying saiddiscriminator in said storage, wherein the discriminator is useable topredict the presence of a mental disorder associated with said firstattribute.
 2. The brain activity analyzing apparatus according to claim1, wherein said first attribute is a disease discriminant label.
 3. Thebrain activity analyzing apparatus according to claim 2, wherein saidattributes of said first subjects include a label of a medicineadministered to the subjects.
 4. The brain activity analyzing apparatusaccording to claim 2, wherein said processor is configured to performdiscrimination of a disease label indicating whether said subject ishealthy or a patient of a neurological/mental disorder.
 5. The brainactivity analyzing apparatus according to claim 1, wherein said brainactivity detecting apparatus includes a plurality of brain activitymeasuring devices; and in sparsely extracting said contractionexpression, said processor is configured to; extract said contractionexpression common in said plurality of first subjects and conditions formeasurement of said plurality of brain activity measuring devices, byvariable selection from correlations of brain activities at saidplurality of prescribed regions.
 6. The brain activity analyzingapparatus according to claim 5, wherein said processor is furtherconfigured to calculate a correlation matrix of activities at saidplurality of prescribed regions from the brain activity signals detectedby a said brain activity measuring device, and extract said contractionexpression by executing the regularized canonical correlation analysisalgorithm between attributes of said first subjects and non-diagonalelements of said correlation matrix.
 7. The brain activity analyzingapparatus according to claim 6, wherein said processor in generatingsaid discriminator by regression of performing further variableselection on said extracted contraction expression and generates thediscriminator by sparse logistic regression on a result of saidregularized canonical correlation analysis and the attributes of saidfirst subjects.
 8. The brain activity analyzing apparatus according toclaim 6, wherein said regularized canonical correlation analysis is acanonical correlation analysis with L1 regularization.
 9. The brainactivity analyzing apparatus according to claim 5, wherein saidplurality of brain activity measuring devices are devices for measuringbrain activities in a time-series by functional brain imaging installedat a plurality of different locations, respectively.
 10. The brainactivity analyzing apparatus according to claim 9, wherein said brainactivity measuring device picks up a resting-state functionalconnectivity magnetic resonance image.
 11. The brain activity analyzingapparatus according to claim 5, wherein the attributes of said firstsubjects include a disease label indicating whether said subject ishealthy or a patient of a neurological/mental disorder, a labelindicating individual nature of said subject, and informationcharacterizing measurement by said brain activity measuring device; andwherein the processor is configured to perform discrimination of adisease label indicating whether said subject is healthy or a patient ofa neurological/mental disorder.
 12. The brain activity analyzingapparatus according to claim 1, wherein said processor is furtherconfigured to; generate said discriminator by regression of performingfurther variable selection on said extracted contraction expression. 13.The brain activity analyzing apparatus according to claim 1, whereinsaid processor is configured to, during a discriminant process, receiveinput data of brain activity signals indicative of brain activity ofsecond subject measured time-sequentially by a brain activity detectingapparatus and execute a discriminant process of said first attribute ofsaid second subject based on the discriminator specified by theinformation stored in said storage.
 14. A brain activity analyzingmethod for a computer including a processing device and a storage deviceto analyze brain activities, comprising: receiving brain activitysignals indicative of brain activity and measured in a time-series by abrain activity detecting apparatus, said brain activity signals beingindicative of brain activities at a plurality of prescribed regions in abrain of each of a plurality of first subjects; storing informationincluding said brain activity signals to said storage device; sparselyextracting, by said processing device using a regularized canonicalcorrelation analysis algorithm, a contraction expression common to saidplurality of first subjects with respect to a plurality of types ofattributes of each of said first subjects, said contraction expressionexpressing a combination of sparsely extracted correlations between saidplurality of prescribed regions as determined from the brain activitysignals stored in said storage device; generating a discriminator toperform a discriminant process of a first attribute among said pluralityof types of attributes based on said extracted correlations; and storinginformation for specifying said discriminator in said storage device,wherein the discriminator is useable to predict the presence of a mentaldisorder associated with said first attribute.
 15. The brain activityanalyzing method according to claim 14, wherein said first attribute isa disease discriminant label.
 16. The brain activity analyzing methodaccording to claim 14, wherein said attributes of subjects include alabel of a medicine administered to the subjects.
 17. The brain activityanalyzing method according to claim 14, wherein said brain activitydetecting apparatus includes a plurality of brain activity measuringdevices; and said generating includes extracting said contractionexpression common in said plurality of first subjects and conditions formeasurement of said plurality of brain activity measuring devices, byvariable selection from correlations of brain activities at saidplurality of prescribed regions.
 18. The brain activity analyzing methodaccording to claim 14, wherein said generating includes calculating saiddiscriminator by regression of performing further variable selection onsaid extracted contraction expression.
 19. The brain activity analyzingmethod according to claim 14, further comprising performing, by saidprocessing device, a discriminant process on input data based on thediscriminator specified by the information stored in said storagedevice.
 20. A biomarker apparatus for generating an output as abiomarker by computer analysis of brain activities, comprising: one ormore processors configured to receive input data of brain activitysignals indicative of brain activity of first subject measuredtime-sequentially and execute a discriminant process of a diseasediscrimination label of said first subject based on a discriminator; andmemory configured to store discriminator information for specifying thediscriminator; wherein said discriminator is, by one or more processors,calculated from brain activity signals stored in said memory, indicativeof brain activity and measured in a time-series by a brain activitydetecting apparatus, said brain activity signals being indicative ofbrain activities at a plurality of prescribed regions in a brain of eachof a plurality of second subjects, by sparsely extracting, using aregularized canonical correlation analysis algorithm, a contractionexpression common to said plurality of second subjects with respect to aplurality of types of attributes of each said second subject, saidcontraction expression expressing a combination of sparsely extractedcorrelations between said plurality of prescribed regions, and generateto perform a discriminant process of a disease discrimination labelamong said plurality of types of attributes based on said extractedcorrelations, wherein the discriminator is useable to produce abiomarker usable to predict the presence of a mental disorder associatedwith said discriminator.
 21. The biomarker apparatus according to claim20, wherein said attributes of subjects include a label of a medicineadministered to the subjects.
 22. The biomarker apparatus according toclaim 20, wherein said brain activity detecting apparatus includes aplurality of brain activity measuring devices; and in sparselyextracting said contraction expression, said contraction expression iscommon in said plurality of second subjects and conditions formeasurement of said plurality of brain activity measuring devices, byvariable selection from correlations of brain activities at saidplurality of prescribed regions.